proteomics analysis (based on normalized ratio)
Siyu Sun
5/4/2020
#read in files #load packages
#read in protein files
#normalized ratios #add the corresponding attributes for time 0 in each condition ##build in ratio from Maxquant, un-normalized
## Using Protein IDs, Gene names, Protein names, Fasta headers as id variables
## Warning: Removed 127128 rows containing non-finite values (stat_boxplot).
## `summarise()` regrouping output by 'channel', 'nutrient', 'samplingTime' (override with `.groups` argument)
## `summarise()` regrouping output by 'channel', 'nutrient', 'samplingTime' (override with `.groups` argument)
## Warning: Ignoring unknown aesthetics: fill
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 127128 rows containing non-finite values (stat_bin).
## Warning: Removed 127128 rows containing non-finite values (stat_density).
## Warning: Removed 46273 rows containing non-finite values (stat_cor).
## Warning: Removed 46273 rows containing missing values (geom_point).
## Warning: Removed 46530 rows containing non-finite values (stat_cor).
## Warning: Removed 46530 rows containing missing values (geom_point).
## Joining, by = c("Protein IDs", "Gene names", "Protein names", "Fasta headers", "channel", "nutrient", "samplingTime", "replicate", "value")
## Warning: Removed 127128 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21176 rows containing non-finite values (stat_boxplot).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 127128 rows containing non-finite values (stat_bin).
## Warning: Removed 127128 rows containing non-finite values (stat_density).
## Warning: Removed 46273 rows containing non-finite values (stat_cor).
## Warning: Removed 46273 rows containing missing values (geom_point).
## Warning: Removed 46530 rows containing non-finite values (stat_cor).
## Warning: Removed 46530 rows containing missing values (geom_point).
## Warning: `as.tibble()` is deprecated as of tibble 2.0.0.
## Please use `as_tibble()` instead.
## The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
## pdf
## 3
## pdf
## 3
## [1] 48 869
## Warning: `select_()` is deprecated as of dplyr 0.7.0.
## Please use `select()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
## pdf
## 3
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 18.9662 13.9459 7.72892 6.56969 4.79319 4.42199 4.17806
## Proportion of Variance 0.4168 0.2254 0.06922 0.05001 0.02662 0.02266 0.02023
## Cumulative Proportion 0.4168 0.6422 0.71141 0.76142 0.78804 0.81070 0.83093
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Standard deviation 4.11177 3.73406 3.38229 3.33381 3.22338 3.15764 2.53271
## Proportion of Variance 0.01959 0.01616 0.01326 0.01288 0.01204 0.01155 0.00743
## Cumulative Proportion 0.85052 0.86667 0.87993 0.89281 0.90485 0.91640 0.92383
## PC15 PC16 PC17 PC18 PC19 PC20 PC21
## Standard deviation 2.39319 2.23829 2.07424 1.95675 1.90651 1.8580 1.77001
## Proportion of Variance 0.00664 0.00581 0.00499 0.00444 0.00421 0.0040 0.00363
## Cumulative Proportion 0.93047 0.93628 0.94126 0.94570 0.94991 0.9539 0.95754
## PC22 PC23 PC24 PC25 PC26 PC27 PC28
## Standard deviation 1.71726 1.6605 1.63241 1.61192 1.56967 1.53783 1.49450
## Proportion of Variance 0.00342 0.0032 0.00309 0.00301 0.00286 0.00274 0.00259
## Cumulative Proportion 0.96096 0.9641 0.96724 0.97025 0.97311 0.97585 0.97843
## PC29 PC30 PC31 PC32 PC33 PC34 PC35
## Standard deviation 1.43294 1.38774 1.38209 1.32895 1.26410 1.2119 1.18132
## Proportion of Variance 0.00238 0.00223 0.00221 0.00205 0.00185 0.0017 0.00162
## Cumulative Proportion 0.98081 0.98304 0.98526 0.98730 0.98916 0.9909 0.99248
## PC36 PC37 PC38 PC39 PC40 PC41 PC42
## Standard deviation 1.15138 1.10257 1.06604 1.0174 0.96542 0.92137 8.423e-15
## Proportion of Variance 0.00154 0.00141 0.00132 0.0012 0.00108 0.00098 0.000e+00
## Cumulative Proportion 0.99401 0.99542 0.99674 0.9979 0.99902 1.00000 1.000e+00
## PC43 PC44 PC45 PC46 PC47
## Standard deviation 3.069e-15 1.334e-15 1.334e-15 1.334e-15 1.334e-15
## Proportion of Variance 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
## Cumulative Proportion 1.000e+00 1.000e+00 1.000e+00 1.000e+00 1.000e+00
## PC48
## Standard deviation 1.334e-15
## Proportion of Variance 0.000e+00
## Cumulative Proportion 1.000e+00
## No scatter3d mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors#Overall, we observed less differential expression between genotypes from PCA plot. Dan suggest a k-means (2-means) cluetering to visualize the data #kmeans clustering of protein groups
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
## Using Gene names as id variables
## Warning: Expected 4 pieces. Additional pieces discarded in 21144 rows [42289,
## 42290, 42291, 42292, 42293, 42294, 42295, 42296, 42297, 42298, 42299, 42300,
## 42301, 42302, 42303, 42304, 42305, 42306, 42307, 42308, ...].
## R1 R2 R3
## AAP1_rim15KO -0.3167888 -0.42748484 -0.219684852
## AAP1_WT -0.3030032 -0.33918557 -0.371384212
## AAT2_rim15KO 0.9436983 0.17967034 -0.003639618
## AAT2_WT -0.1268524 -0.03253460 -0.398065497
## ABF2_rim15KO 0.1049084 0.46948828 0.527496729
## ABF2_WT -0.1570608 -0.07698831 0.240618228
## No scatter3d mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## List of 9
## $ cluster : Named int [1:1762] 1 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "names")= chr [1:1762] "AAP1_rim15KO" "AAP1_WT" "AAT2_rim15KO" "AAT2_WT" ...
## $ centers : num [1:2, 1:3] -0.1068 5.1186 -0.0908 4.3541 -0.1103 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "1" "2"
## .. ..$ : chr [1:3] "R1" "R2" "R3"
## $ totss : num 5283
## $ withinss : num [1:2] 1062 1534
## $ tot.withinss: num 2596
## $ betweenss : num 2687
## $ size : int [1:2] 1726 36
## $ iter : int 1
## $ ifault : int 0
## - attr(*, "class")= chr "kmeans"
## `summarise()` regrouping output by 'k2.cluster' (override with `.groups` argument)
## # A tibble: 4 x 3
## # Groups: k2.cluster [2]
## k2.cluster genotype `n()`
## <int> <chr> <int>
## 1 1 rim15KO 853
## 2 1 WT 873
## 3 2 rim15KO 28
## 4 2 WT 8#normalize every data point in relative to time 0 ##look at the logFC for both protein
## Parsed with column specification:
## cols(
## .default = col_double(),
## `Protein IDs` = col_character(),
## `Protein names` = col_character(),
## `Gene names` = col_character(),
## `Fasta headers` = col_character()
## )
## See spec(...) for full column specifications.
## Using Protein IDs, Protein names, Gene names, Fasta headers as id variables
## Using Protein IDs, Protein names, Gene names, Fasta headers, cond as id variables
## Warning: Removed 29867 row(s) containing missing values (geom_path).
## Using Protein IDs, Protein names, Gene names, Fasta headers, nutrient, time as id variables
## Warning: Expected 2 pieces. Additional pieces discarded in 34542 rows [69085,
## 69086, 69087, 69088, 69089, 69090, 69091, 69092, 69093, 69094, 69095, 69096,
## 69097, 69098, 69099, 69100, 69101, 69102, 69103, 69104, ...].
## Warning: `summarise_each_()` is deprecated as of dplyr 0.7.0.
## Please use `across()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
## Warning: `funs()` is deprecated as of dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
## Using Protein IDs, Protein names, Gene names, Fasta headers, nutrient, time as id variables
## Warning: Expected 2 pieces. Additional pieces discarded in 34542 rows [69085,
## 69086, 69087, 69088, 69089, 69090, 69091, 69092, 69093, 69094, 69095, 69096,
## 69097, 69098, 69099, 69100, 69101, 69102, 69103, 69104, ...].
## Warning: Removed 21 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 21 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
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## `summarise()` regrouping output by 'nutrient', 'time' (override with `.groups` argument)
#Now, let’s see what are the differentially expressed proteins between conditions overtime for each genotype
## Parsed with column specification:
## cols(
## .default = col_double(),
## `Protein IDs` = col_character(),
## `Protein names` = col_character(),
## `Gene names` = col_character(),
## `Fasta headers` = col_character()
## )
## See spec(...) for full column specifications.
## Using Protein IDs, Protein names, Gene names, Fasta headers as id variables
## Using Protein IDs, Protein names, Gene names, Fasta headers, genotype, time as id variables
## Using Protein IDs, Protein names, Gene names, Fasta headers, genotype, time as id variables
## Warning: Removed 12 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 10 rows containing missing values (geom_point).
## Warning: Removed 12 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 10 rows containing missing values (geom_point).
## `summarise()` regrouping output by 'genotype', 'time' (override with `.groups` argument)